System and method for color calibrating an image

ABSTRACT

Systems and methods for color calibrating an image are described herein. In some embodiments, a method includes disposing a unique identifier and a fiducial marker that includes a plurality of color regions on an object. A first image of the object is captured with a first camera and a second image of the object that is captured with a second camera is received. The second image is associated with the first image based on the unique identifier and spectral information of at least a portion of the fiducial marker in the second image is compared with spectral information of at least the portion of the fiducial marker in the first image. Based on the comparison, the second image is resampled to substantially match at least the portion of the fiducial marker in the second image to at least the portion of the fiducial marker in the first image.

BACKGROUND

Embodiments described herein relate generally to systems and methods forcolor calibrating an image, and in particular to color calibrating animage for qualitative and/or quantitative image analysis.

A wide variety of applications (e.g., medical diagnostics, security, andremote sensing) implement techniques to extract meaningful informationfrom captured images. More specifically, meaningful information can beextracted by analyzing and/or measuring colors in a captured image.However, images are often captured in an uncontrolled manner. That is,images may be captured using different cameras and under differentsettings (e.g., lighting, fixtures, etc.). So, two cameras capturing animage of the same object may produce different results making itchallenging to analyze these images or measure the colors in thesecaptured images.

Hence, there is an unmet need to provide a system and method to controland calibrate an image that is agnostic to the type of camera or theenvironment in which the image is captured.

SUMMARY

Systems and methods for color calibrating an image are described herein.In some embodiments, a method includes disposing a unique identifier anda fiducial marker that includes a plurality of color regions on anobject. A first image of the object is captured with a first camera anda second image of the object that is captured with a second camera isreceived. The second image is associated with the first image based onthe unique identifier and spectral information of at least a portion ofthe fiducial marker in the second image is compared with spectralinformation of at least the portion of the fiducial marker in the firstimage. Based on the comparison, the second image is resampled tosubstantially match the first image.

In some embodiments, a method includes collecting reliable data relatingto a test associated with the diagnostic assay. The method includesdisposing a unique identifier on a diagnostic assay, disposing aspectral fiducial that includes a plurality of color regions on thediagnostic assay, and capturing with a first camera a first image of thediagnostic assay. The method further includes at a processor, receivinga second image of the diagnostic assay that is captured with a secondcamera, associating the second image with the first image based on theunique identifier, comparing spectral information of at least a portionof the spectral fiducial in the second image with spectral informationof at least the portion of the spectral fiducial in the first image,resampling the second image to substantially match the first image basedon the comparison, and correlating spectral information of at least aportion of the resampled second image to corresponding informationrelating to the test to collect reliable data.

In some embodiments a system includes a server processor and amanufacturing unit in digital communication with the server processor.The manufacturing unit is configured to (a) dispose a unique identifieron an object, (b) dispose a spectral fiducial on the object, thespectral fiducial including a plurality of color regions, (c) capture afirst image of the object with an image capturing device, and (d)transmit the first image to the server processor. The server processoris configured to (a) receive a second image of the object from a remoteimage capturing device, (b) associate the second image with the firstimage based on the unique identifier, (c) compare spectral informationof at least a portion of the spectral fiducial in the second image withspectral information of at least the portion of the spectral fiducial inthe first image, and (d) resample the second image to substantiallymatch the first image based on the comparison.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts discussed in greater detail below (provided suchconcepts are not mutually inconsistent) are contemplated as being partof the inventive subject matter disclosed herein. In particular, allcombinations of claimed subject matter appearing at the end of thisdisclosure are contemplated as being part of the inventive subjectmatter disclosed herein. It should also be appreciated that terminologyexplicitly employed herein that also may appear in any disclosureincorporated by reference should be accorded a meaning most consistentwith the particular concepts disclosed herein.

Other systems, processes, and features will become apparent to thoseskilled in the art upon examination of the following drawings anddetailed description. It is intended that all such additional systems,processes, and features be included within this description, be withinthe scope of the present invention, and be protected by the accompanyingclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The skilled artisan will understand that the drawings primarily are forillustrative purposes and are not intended to limit the scope of theinventive subject matter described herein. The drawings are notnecessarily to scale; in some instances, various aspects of theinventive subject matter disclosed herein may be shown exaggerated orenlarged in the drawings to facilitate an understanding of differentfeatures. In the drawings, like reference characters generally refer tolike features (e.g., functionally similar and/or structurally similarelements).

FIG. 1 illustrates an overview of an image calibrating system, accordingto an embodiment.

FIGS. 2A-2C illustrate the process of manufacturing an object for imagecalibration in accordance with some embodiments.

FIG. 3 illustrates a manufactured object for image calibration,according to an embodiment.

FIGS. 4A-4B illustrate capturing a pre-image and a captured image,according to an embodiment.

FIG. 5 is a flowchart illustrating a method, according to an embodiment.

DETAILED DESCRIPTION

The present disclosure describes systems and methods for colorcalibrating an image in a manner that is agnostic to the type of imagecapturing device and/or the environment in which the image is captured.

Image-based analysis is often performed to extract meaningfulinformation from captured images. However, images may not always becaptured in a standard manner. For example, different cameras can havedifferent settings and/or the users of the cameras can use differentpositioning or focal points, resulting in vastly different images of thesame object. Similarly, the same camera may capture images at adifferent time with different settings, positioning, focal point, etc.In addition, the environment in which the images are captured can affectthe captured image. For example, lighting or shadows can make imagesthat are captured from the same camera seem dramatically different.Thus, two cameras capturing an image of the same object might producetwo different results. Likewise, the same camera capturing two differentimages of the same object at a different time may produce two differentresults.

Embodiments described herein disclose systems and methods to modify acaptured image irrespective of the type of device used to capture theimage or the environment in which the image is taken. In someembodiments, a unique identifier and a fiducial marker (also referred toas “spectral fiducial”) are disposed on an object during or aftermanufacturing of the object. The unique identifier can be used toreference captured images of the object and/or to associate capturedimages of the object with other images of the object or the objectitself. The fiducial marker can be used as a point of reference and/orto calibrate captured images of the object. The facility/unit tomanufacture the object can include a first image capturing device. Themanufacturing unit with the first image capturing device can beconfigured to capture an image of the object in a controlled manner.That is, the image is captured with a known image capturing device(first image capturing device) and under known settings. In addition,images of other objects may also be captured using the same imagecapturing device (first image capturing device) and under the samesettings.

The image from the manufacturing unit captured in a controlled manner(also referred to as “pre-image”) is transmitted to a processor/server.The server references the pre-image with the unique identifier andstores the pre-image in a database and/or memory. After the pre-image istransmitted from the manufacturing unit to the server, the object isdispatched and is obtained by an end user. The dispatched object may ormay not be used by the end user. That is, the end user may change aportion of the object. However, the unique identifier and spectralfiducial disposed on the object does not change significantly. A secondimage capturing device (also referred to as a “remote image capturingdevice”) associated with the end user can be used to capture an image(i.e., a picture) of the dispatched object. The second image capturingdevice can be used to capture the image in an uncontrolled manner. Morespecifically, the second image capturing device can be any type of imagecapturing device and the image can be captured in under any setting. Theimage from the second image capturing device captured in an uncontrolledmanner (also referred to as “captured image”) can then be transmitted tothe server.

The server can be configured to access the pre-images stored in thememory and associate the unique identifier in the captured image withthe unique identifier in the pre-image. The server can then compare thespectral fiducial and/or a portion of the spectral fiducial in thecaptured image with the spectral fiducial and/or the correspondingportion of the spectral fiducial in the pre-image. Based on thiscomparison, the server can be configured to resample the captured imageto substantially match the pre-image. In other words, the server canmodify the captured images color-correct and/or otherwise modify thecaptured image such that the color, exposure, white balance, etc. tosubstantially match the pre-image. In this manner, images captured froman “uncontrolled” camera can be modified as if they were captured in acontrolled environment with controlled settings. Reliable informationcan then be extracted from the resampled captured image. Thus,information from the image can be extracted such that the type of theimage capturing device or the environment in which the image is captureddoes not affect the reliability of the information.

FIG. 1 is a schematic overview of an image calibrating system 100,according to some embodiments. The image calibrating system 100 includesa manufacturing unit/sub-system and/or a production facility 110 thatincludes a first image capturing device (not shown) a second imagecapturing device 120, and a server processor 130. The manufacturing unit110 and the first image capturing device are in digital communicationwith the server processor 130, and the server processor 130 is indigital communication with the second image capturing device 120. Insome embodiments, the manufacturing unit 110 can be a facility and/orsystem that manufactures the object. In some embodiments, themanufacturing unit 110 can be a facility and/or system that obtains themanufactured object and subsequently applies steps to capture thepre-image of the object as described below.

The manufacturing unit 110 can be configured to provide a uniqueidentifier to an object such that the object, any image of the objectand/or any information related to the object can be tracked. The uniqueidentifier can be a barcode, a hologram, magnetic ink characters,combinations thereof, and/or the like. In some embodiments, the uniqueidentifier can be formed by a random pattern of particles disposed on asubstrate such as a label. In some embodiments, the unique identifiercan be a serialization code, a bar code, a QR code, or a human readablealphanumeric code that is electronically printed on the object. Theunique identifier can be disposed on an adhesive label and the adhesivelabel is disposed on the object. The unique identifier facilitatestracking of the object, images of the object, and/or any informationspecifically related to the object. In some embodiments, the uniqueidentifier can include anti-counterfeiting measures configured to ensurethe authenticity of the object. In some embodiments, the manufacturingunit 110 disposes the unique identifier during the manufacture of theobject. In some embodiments, the manufacturing unit 110 disposes theunique identifier following the manufacture of the object.

The manufacturing unit 110 can also be configured to dispose a spectralfiducial on the object such that the spectral fiducial forms a basis forcomparing images of the object. In some embodiments, the fiducial markeris a spectrum of colors or a scheme of spectral fiducial. The spectralfiducial can include a plurality of color regions. In some embodiments,the color regions can be separate and distinct. The spectral fiducialcan be arbitrary blobs of colors. In some embodiments, the spectralfiducial can include multiple shades of the same color. In someembodiments, the spectral fiducial can include different colors. In someembodiments, one portion of the spectral fiducial can include multipleshades of the same color while a different portion includes differentcolors. In some embodiments, the fiducial marker can be a grayscaleimage that is electronically printed on the object. In some embodiments,the fiducial marker can be any arbitrary intrinsic component of theobject being imaged. In some embodiments, the manufacturing unit 110disposes the fiducial marker during the manufacture of the object. Insome embodiments, the manufacturing unit 110 disposes the fiducialmarker following the manufacture of the object. In some embodiments, thefiducial marker can be disposed on an adhesive label and the adhesivelabel can be disposed on the object. In some embodiments, the spectralfiducial is printed directly on the object.

As described herein, the spectral fiducial can be utilized to compareand correct images of an object. In some embodiments, the spectralfiducial can be utilized to compare and correct a portion of an image(also referred to as “region of interest” or “ROI”). In other words, thespectral fiducial can be utilized to compare and correct only the regionof interest rather than correcting the entire image of the object. Insome embodiments, the spectral fiducial can include discreet hues of asingle color or a range of colors such that correction of images can becentered around a specific series of wavelengths to provide a morerobust and accurate method for correcting spectra within the region ofinterest to the targeted wavelengths in the spectral fiducial and/ormultiple spectral fiducials. Thus, practical constraints of space andresolution can be overcome by utilizing the space available on the imageto provide discreet colors such that the targeted spectra within the ROIcan be ideally represented.

The manufacturing unit 110 includes a first image capturing device (notshown) that is configured to capture a pre-image of the object. Thefirst image capturing device along with the manufacturing unit 110 canbe configured to capture the pre-image of the object in a controlledmanner. That is, the manufacturing unit 110 uses a known image capturingdevice to capture the pre-images under known settings (e.g., controlledlighting and fixtures). Additionally, the same image capturing devicecan be used to capture pre-images of every object in a stable andcontrolled environment (e.g., controlled lighting and fixtures). Saidanother way, a pre-image of every object is captured by the same imagecapturing device under the exact same setting. In some embodiments, thefirst image capture device can be configured to take multiple images ofthe object and the manufacturing unit 110 can be configured to determinewhich image or images should be stored for later comparison.

In some embodiments, the first image capturing device can be stationaryequipment, such as a flat bed scanner. In some embodiments, the firstimage capturing device can be a portable device such as a handheldcomputer tablet, a smartphone with camera, or a digital camera. In someembodiments, the first image capturing device includes or is otherwiseconfigured to utilize an adapter or other attachment to a mobileelectronic device, such as a phone or tablet, to capture the pre-image.In this manner, at least a portion of the first image capturing deviceis generally mobile and can easily be transported between and/or aroundshipping vessels, warehouses, or other locations, for manufacturingobjects at various locations in the supply chain. In some embodiments,the first image capturing device can be substantially stationary. Thepre-image can be an image of the entire object including the uniqueidentifier and the spectral fiducial. The pre-image is transmitted fromthe manufacturing unit 110 to the server processor 130. In someembodiments, the manufacturing unit 110 additionally sends informationthat is necessary for analyzing captured images of the object and forcollecting reliable data from captured images (e.g., data relating tocalibrating and correcting images).

In some embodiments, the second image capturing device 120 is associatedwith an end user. In some embodiments, the second image capturing devicecan be a portable device such as a handheld computer tablet, asmartphone with camera, or a digital camera. In some embodiments, thesecond image capturing device includes or is otherwise configured toutilize an adapter or other attachment to a mobile electronic device,such as a phone or tablet. In this manner, at least a portion of thesecond image capturing device is generally mobile and can easily betransported between various locations. In some embodiments, the secondimage capturing device can be substantially stationary. As describedherein, the second image capturing device 120 captures the image in anuncontrolled manner. That is, images are captured using different typesof image capturing devices in different settings and thus, the imagescan be captured in a device and environment agnostic manner. Capturedimages of the object can be transmitted from the second image capturingdevice 120 to the server processor 130 for calibration and analysis.

The server processor 130 is in digital communication (e.g., wired orwireless) with the manufacturing unit 110 and the second image capturingdevice 120. The server processor 130 can be configured to calibrate acaptured image. Information (e.g., pre-images and information relatingto calibrating captured images) are sent from the manufacturing unit 110to the server processor 130. The server processor 130 can include atleast one storage unit (e.g., memory, database, etc.) to storeinformation obtained from the manufacturing unit 110. In someembodiments, when pre-images are transmitted from the manufacturing unit110 to the server processor 130, the server processor 130 can beconfigured to reference the pre-image with the unique identifierdisposed on the pre-image. The pre-image is then stored in the storageunit along with the reference.

Upon obtaining a captured image from the second image capturing device120, the server processor 130 can be configured to access the storageunit to associate the unique identifier in the captured image to theunique identifier in a pre-image. As discussed in greater detail below,the server processor 130 can compare spectral information in thecaptured image to the spectral information in the pre-image and resamplethe captured image to substantially match the pre-image. Morespecifically, the server processor 130 can be configured to compare atleast a portion of the spectral fiducial in the captured image to acorresponding portion of the spectral fiducial in the pre-image. Theserver processor 130 can be configured to resample the captured imagebased on this comparison such that the spectral fiducial on the capturedimage substantially matches the spectral fiducial on the pre-image. Insome embodiments, the server processor 130 can correlate the colors onthe resampled captured image to the spectral fiducial of the resampledcaptured image. In some embodiments, the server processor 130 cancorrelate spectral information of the resampled image with thecorresponding information specific to the object that was previouslytransmitted from the manufacturing unit 110 and stored in the storageunit to collect reliable data. The corresponding information can beinformation relating to calibrating captured images of the object. Somenon-limiting examples of spectral information can include intensity,wavelength, spectral value, Doppler shift, Zeeman splitting,combinations thereof, and/or the like. In some embodiments, the serverprocessor 130 is a cloud-based server processor.

FIGS. 2A-2C illustrate manufacturing of an object 240, according to anembodiment. A manufacturing or production facility (e.g., manufacturingunit 110 in FIG. 1) can be configured to manufacture the object 240.Captured images of the object 240 can be calibrated and analyzed toextract meaningful quantitative and/or qualitative information.

In some embodiments, the object 240 is a diagnostic assay. In someembodiments, the object 240 is a lateral flow immunochromatographicassay intended to detect the presence or absence of an analyte. In someembodiments, the lateral flow immunochromatographic assay can includesample pad, a conjugate pad, a membrane, a wicking pad, a test area 255a, and a control area 255 b. An image of a portion 290 of the object 240including the sample pad, conjugate pad, membrane, wicking pad, testarea 255 a and control area 255 b together form the portion of the imageof the object that can be defined as a region of interest. The lateralflow immunochromatographic assay includes a series of capillary bedsthat transports fluids. The fluid in chemical combination with particlesthat are already present in the assay are transported through the assayvia capillary action. When the fluid passes through the test area 255 aand the control area 255 b, particles accumulate and the test area 255 aand the control area 255 b change color. In this manner, the presence orabsence of an analyte can be detected by analyzing the color in the testarea 255 a and the control area 255 b. In some embodiments, a colorchange can simply indicate the presence of an analyte. In other words,some immunochromatographic assays are “binary” in that they are simplyconfigured to detect whether or not an analyte is present or not. Insome embodiments, an immunochromatographic assay can be configured todetect the amount of the analyte present. The amount of the analytepresent can be determined by measuring the intensity of the color in thetest area 255 a and the control area 255 b.

Referring to FIG. 2A, the object 240 can include a test area 255 a and acontrol area 255 b. The test area 255 a and the control area 255 b inthe object 240 can be configured so as to provide qualitativeinformation relating to a specific test. Qualitative information fromthe object 240 can be extracted based on the color of the test area 255a and the control area 255 b. In some embodiments, the intensity of thecolor in the test area 255 a and the control area 255 b can be measuredto extract quantitative information relating to that specific test. Forlateral flow immunochromatographic assays, the color of the test area255 a can be correlated to the amount of analyte present thus providingmeaningful quantitative information. Thus, the image of the portion 290of the object is the ROI that provides meaningful information relatingto the specific test.

Referring to FIG. 2B, the manufacturing or production facility can beconfigured to dispose a unique identifier 260 on the object 240. Theunique identifier 260 can be a barcode, a hologram, magnetic inkcharacters, combinations thereof, and/or the like. In some embodiments,the unique identifier 260 can be formed by a random pattern of particlesdisposed on a substrate such as a label. In some embodiments, the uniqueidentifier 260 can be a serialization code, a bar code, a QR code, or ahuman readable alphanumeric code that is electronically printed on theobject. The unique identifier 260 can be disposed on an adhesive labeland the adhesive label is disposed on the object. The unique identifier260 facilitates tracking of the object, images of the object, and/or anyinformation specifically related to the object. In some embodiments, theunique identifier 260 can include anti-counterfeiting measuresconfigured to ensure the authenticity of the object.

Referring to FIG. 2C, the manufacturing or production facility can beconfigured to dispose a spectral fiducial 270 on the object 240. In someembodiments, the spectral fiducial 270 is a spectrum of colors or ascheme of spectral fiducial 270. The spectral fiducial 270 can include aplurality of color regions. In some embodiments, the color regions canbe separate and distinct. The spectral fiducial 270 can be arbitraryblobs of colors. In some embodiments, the spectral fiducial 270 caninclude multiple shades of the same color. In some embodiments, thespectral fiducial 270 can include different colors. In some embodiments,one portion of the spectral fiducial 270 can include multiple shades ofthe same color while a different portion includes different colors. Insome embodiments, the spectral fiducial 270 can be a grayscale imagethat is electronically printed on the object. In some embodiments, thefiducial marker 270 can be any arbitrary intrinsic component of theobject being imaged. In some embodiments, the spectral fiducial 270 canbe disposed on an adhesive label and the adhesive label can be disposedon the object. In some embodiments, the spectral fiducial 270 is printeddirectly on the object.

As described herein, the spectral fiducial 270 can be utilized tocompare and correct images of an object. In some embodiments, it may bedesirable to only correct an image of a portion of the object 240 (e.g.,a region of interest). For instance, in a diagnostic assay or lateralflow immunochromatographic assay, an image of the portion 290 of theassay 240 including the test area 255 a and control area 255 b can becompared and corrected to extract meaningful information. Thus, thespectral fiducial can be utilized to compare and correct a portion of animage (e.g., ROI) of the assay (e.g., object 240) rather than the entireassay. In order to utilize space available on the object 240, thespectral fiducial 270 can include discreet hues of a single color or arange of colors such that correction of images can be centered aroundspecific series of wavelengths. This provides for a more robust andaccurate method as far as correcting spectra within the region ofinterest to the targeted wavelengths on the spectral fiducial 270. Thatis, the range of colors and their associated wavelengths can beconfigured such that targeted portions of the ROI can be compared andcorrected in an accurate and robust manner even if the rest of the imagemay not be corrected accurately.

For example, in a test associated with the diagnostic assay or lateralflow immunochromatographic assay that turns red in the presence orabsence of an analyte, the spectral fiducial 270 can be configured suchthat the discreet colors included in the spectral fiducial 270 areshades and/or hues of red. In such a case, when a captured image of theassay is compared and corrected with the corresponding pre-image basedon the spectral fiducial 270, portions of the ROI in the resampledcaptured image may look slightly different from the pre-image. However,as long as the “reds” in the test area 255 a and/or control area 255 bare accurately represented, the result of the test will be accurate.Said another way, by targeting only shades/hues of red, the colorcorrection may be less accurate for blue, green, etc. at the expense ofaccuracy “reds”. However, a spectral fiducial of all of the visiblecolors can result in a poor sample size of “red” colors, so the colorcorrection be less accurate for a particular range of wavelengths (e.g.,reds) even though it will generally be more accurate for the broadvisible spectrum.

Therefore, if a test is configured such that the color change of testarea 255 a in a particular range of wavelengths indicates analyteconcentration (e.g., from light pink when low concentrations of ananalyte is present, to a darker red when higher concentrations of theanalyte is present) providing a spectral fiducial 270 that includes avariety of shades and/or hues in that particular range of wavelengthscan allow for a more accurate color correction of the image. The moreaccurate color correction of the image can be used to provide a moreaccurate determination of analyte concentration in the sample. FIG. 3illustrates an object 340 manufactured at the manufacturing unit 110 inFIG. 1, according to an embodiment. The object 340 includes a uniqueidentifier 360 and a spectral fiducial 370 disposed on an adhesive label380. The unique identifier 360 can be substantially similar in formand/or function to the unique identifier 260 and thus, some aspects ofthe unique identifier 360 are not described in further detail herein.Similarly, the spectral fiducial 370 can be substantially similar inform and/or function to the spectral fiducial 270 and thus, some aspectsof the spectral fiducial 370 are not described in further detail herein.In some embodiments, the object 340 includes a unique identifier 360 anda spectral fiducial 370 disposed on an adhesive label 380. In someembodiments, the object 340 is a diagnostic assay including a test area355 a and a control area 355 b. Qualitative information can be extractedfrom the object 340 itself or from an image of the object 340 byanalyzing and detecting the color of the portion 390 of the object 340including the test area 355 a and/or the control area 355 b. Asdescribed above, the intensity of the color in the portion 390 of theobject 340 that includes the test area 355 a and/or the control area 355b can be measured to extract quantitative information by analyzing theobject 340 itself or by analyzing an image of the object 340.

FIGS. 4A and 4B illustrate capturing a pre-image 490 of an object 440 ata first time period and capturing a captured image 490′ of the object440′ at a second time period, according to an embodiment. The object 440and 440′ can be substantially similar in form and/or function to theobject 240 or 340 and thus, some aspects of the object 440 are notdescribed in further detail herein. A manufacturing or productionfacility (e.g., manufacturing unit 110 in FIG. 1) can include a firstimage capturing device 415 configured to capture the pre-image 490 ofthe object in a controlled manner. That is, the pre-image 490 iscaptured by a known image capturing device 415 under controlled (i.e.,known) settings. The image capturing device 415 can be substantiallysimilar in form and/or function to the image capturing device describedabove and thus, some aspects of the image capturing device 415 are notdescribed in further detail herein. In addition, all pre-images may becaptured by the first image capturing device 415 under samesettings/conditions. In some embodiments, the pre-image 490 is the imageof the manufactured object 340 in FIG. 3 captured by the first imagecapturing device 415 at a manufacturing or production facility. Thepre-image 490 can be transmitted from the first image capturing device415 to a server processor (e.g., server processor 130 in FIG. 1). Theserver processor can be configured to reference the pre-image 490 with aunique identifier 460 on the pre-image 490 and to store the informationin a memory and/or a database.

In some embodiments, after the pre-image 490 is transmitted from themanufacturing or production facility to the server processor, the objectis dispatched and is obtained by an end user. As shown in FIG. 4B,captured image 490′ from a second image capturing device 420 that isassociated with the end user can be transmitted to the server processor.The second image capturing device 420 (also referred to as “remote imagecapturing device”) can be substantially similar in form and/or functionto the second image capturing device 120 described above and thus, someaspects of the second image capturing device 420 are not described infurther detail herein. In some embodiments, the captured image 490′ canbe the image of the manufactured object 340 in FIG. 3 obtained and/orused by the end user and captured by the second image capturing device420. Once the captured image 490′ is transmitted to the serverprocessor, the server processor can be configured to associate theunique identifier 460′ in the captured image 490′ with the uniqueidentifier 460 in the pre-image 490 by accessing stored pre-images inthe memory and/or database. Additionally, the server processor cancompare the spectral fiducial 470′ in the captured image 490′ with thespectral fiducial 470 in the pre-image 490 to resample the capturedimage 490′ to substantially match the pre-image 490. In this manner,reliable information can be extracted from the captured image 490′ suchthat the environment in which the captured image 490′ is taken and thetype of the image capturing device does not affect the information thatis extracted.

FIG. 5 illustrates a method 500, according to an embodiment. In someembodiments, the method 500 is implemented by the image calibratingsystem 100 disclosed herein. The method 500 includes, at step 510,disposing a unique identifier (e.g., unique identifier 360 in FIG. 3) onan object. At step 520, a fiducial marker (e.g., spectral fiducial 370in FIG. 3) is disposed on the object. In some embodiments, the fiducialmarker is a spectrum of colors or a scheme of spectral fiducial. Thespectral fiducial can be arbitrary blobs of colors. At 530, a firstimage (e.g., pre-image 490 in FIG. 4A) of the object is captured with afirst camera (e.g., first image capturing device 415 in FIG. 4A). Thefirst image is captured in a controlled manner. Said another way, thefirst image is captured by a known first camera and in a knownenvironment. In some embodiments, the spectral values of the colors inthe spectral fiducial are printed on the object.

The first image is transmitted to a server system (e.g., serverprocessor 130 in FIG. 1). In some embodiments, the server system is acloud based server system. The server system references the first imagewith the unique identifier and stores the first image in a databaseand/or a memory. At 540, the server system receives a second image(e.g., captured image 490′ in FIG. 4B) of the object that is capturedwith a second camera (e.g., second image capturing device 420 in FIG.4B). The second image can be captured without qualifying the secondcamera. That is, the second image can be captured in an uncontrolledmanner. At 550, the server system associates the unique identifier onthe second image with the unique identifier on the first image byaccessing the unique identifier and/or the first image from the databaseand/or memory. At 560, the server system compares spectral informationof the fiducial marker in the second image with spectral information ofthe fiducial marker in the first image. Some non-limiting examples ofspectral information can include intensity, wavelength, spectral value,Doppler shift, Zeeman splitting, combinations thereof, and/or the like.In some embodiments, the server system compares a portion of thefiducial marker in the second image with the corresponding portion ofthe fiducial marker in the first image. Thus, by analyzing and/orcomparing only a portion of the first image and second image, the entiresecond image can be corrected. At 570, based on the comparison in step560, the second image is resampled. That is, by comparing at least aportion of the fiducial marker in the second image to the correspondingportion of the fiducial marker in the first image, the fiducial markerin the second image is transformed to substantially match the fiducialmarker in the first image. Thus, the entire second image is resampledsuch that the fiducial marker in the second image substantially matchesthe fiducial marker in the first image.

In some embodiments, the method 500 can further include correcting thesecond image for any physical distortions by applying lineartransformation of position vectors, such as affine transformation on thesecond image. The server system can be further configured to linearizethe color channels and/or the blobs of colors in the spectral fiducialby applying linear transformation on the spectral fiducial portion ofthe second image.

In some embodiments, the object is a diagnostic assay and the serversystem can be configured to analyze the second image to collectquantifiable data relating to a test associated with the diagnosticassay. The diagnostic assay can be an immunochromatographic assay thatis used for home testing, point of care testing, laboratory testing,and/or a combination thereof. The diagnostic assay can include samplepad, conjugate pad, membrane, wicking pad, test area, and control area.In some embodiments, information relating to the test (e.g., testvalues) are stored in the database and/or memory of the server system.The server system can be configured to extract quantifiable data fromthe second image by correlating the information relating to the testwith the resampled second image. For instance, the server system canapply curve fitting formula on the region of interest including the testarea and the control area in the resampled second image. By applying thecurve fitting formula, the sever system can correlate the spectralinformation in the resampled second image (e.g., ROI of the resampledsecond image) with the corresponding information relating to the test(e.g., test values etc.) that is stored in the database and/or thememory. For example, for a diagnostic assay, resampling can includecorrelating the spectral information in the ROI with properties of ananalyte that may have influenced the spectral content of the ROI. Insome embodiments, resampling can include color correction, white/graybalancing, gamma correction, affine image transformation (e.g., scale,shear, rotation, and translation), combinations thereof, and/or thelike.

In some embodiments, the second camera can include a software to guidean end user to capture the second image. The software may ensure thatthe second image meets a minimum quality before transmitting the secondimage to the server system.

In this manner, reliable actionable data can be collected from an imagewithout qualifying the device with which the image is captured. Thus,the color calibration technique disclosed herein is not affected bycamera to camera variability or the environment in which an image iscaptured.

Some embodiments described herein relate to a computer storage productwith a non-transitory computer-readable medium (also referred to as anon-transitory processor-readable medium) having instructions orcomputer code thereon for performing various computer-implementedoperations. The computer-readable medium (or processor-readable medium)is non-transitory in the sense that it does not include transitorypropagating signals (e.g., a propagating electromagnetic wave carryinginformation on a transmission medium such as space or a cable). Themedia and computer code (also referred to herein as code) may be thosedesigned and constructed for the specific purpose or purposes. Examplesof non-transitory computer-readable media include, but are not limitedto: flash memory, magnetic storage media such as hard disks, opticalstorage media such as Compact Disc/Digital Video Discs (CD/DVDs),Compact Disc-Read Only Memories (CD-ROMs), magneto-optical storage mediasuch as optical disks, carrier wave signal processing modules, andhardware devices that are specially configured to store and executeprogram code, such as Application-Specific Integrated Circuits (ASICs),Programmable Logic Devices (PLDs), Read-Only Memory (ROM) andRandom-Access Memory (RAM) devices.

Examples of computer code include, but are not limited to, micro-code ormicro-instructions, machine instructions, such as produced by acompiler, code used to produce a web service, and files containinghigher-level instructions that are executed by a computer using aninterpreter. For example, embodiments may be implemented using Java,C++, or other programming languages and/or other development tools.

Where methods and/or schematics described above indicate certain eventsand/or flow patterns occurring in certain order, the ordering of certainevents and/or flow patterns may be modified. Additionally certain eventsmay be performed concurrently in parallel processes when possible, aswell as performed sequentially.

Also, various inventive concepts may be embodied as one or more methods,of which an example has been provided. The acts performed as part of themethod may be ordered in any suitable way. Accordingly, embodiments maybe constructed in which acts are performed in an order different thanillustrated, which may include performing some acts simultaneously, eventhough shown as sequential acts in illustrative embodiments.

All definitions, as defined and used herein, should be understood tocontrol over dictionary definitions, definitions in documentsincorporated by reference, and/or ordinary meanings of the definedterms.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, a reference to “A and/or B”, when usedin conjunction with open-ended language such as “comprising” can refer,in one embodiment, to A only (optionally including elements other thanB); in another embodiment, to B only (optionally including elementsother than A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

As used herein in the specification and in the claims, “or” should beunderstood to have the same meaning as “and/or” as defined above. Forexample, when separating items in a list, “or” or “and/or” shall beinterpreted as being inclusive, i.e., the inclusion of at least one, butalso including more than one, of a number or list of elements, and,optionally, additional unlisted items. Only terms clearly indicated tothe contrary, such as “only one of” or “exactly one of,” or, when usedin the claims, “consisting of,” will refer to the inclusion of exactlyone element of a number or list of elements. In general, the term “or”as used herein shall only be interpreted as indicating exclusivealternatives (i.e. “one or the other but not both”) when preceded byterms of exclusivity, such as “either,” “one of,” “only one of,” or“exactly one of.” “Consisting essentially of,” when used in the claims,shall have its ordinary meaning as used in the field of patent law.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

In the claims, as well as in the specification above, all transitionalphrases such as “comprising,” “including,” “carrying,” “having,”“containing,” “involving,” “holding,” “composed of,” and the like are tobe understood to be open-ended, i.e., to mean including but not limitedto. Only the transitional phrases “consisting of” and “consistingessentially of” shall be closed or semi-closed transitional phrases,respectively, as set forth in the United States Patent Office Manual ofPatent Examining Procedures, Section 2111.03.

The invention claimed is:
 1. A method comprising: disposing a uniqueidentifier on an object; disposing a fiducial marker on the object, thefiducial marker including a plurality of color regions; capturing afirst image of the object with a first camera; receiving a second imageof the object, the second image captured with a second camera;associating the second image with the first image based on the uniqueidentifier; comparing spectral information of at least a portion of thefiducial marker in the second image with spectral information of atleast the portion of the fiducial marker in the first image; andresampling the second image to match at least the portion of thefiducial marker in the second image to at least the portion of thefiducial marker in the first image based on the comparison.
 2. Themethod of claim 1, further comprising: storing the first image in acloud based server system; and referencing the first image in the cloudbased server system with the unique identifier.
 3. The method of claim1, wherein disposing the fiducial marker includes printing the pluralityof color regions on the object.
 4. The method of claim 1, furthercomprising: correcting the second image for any physical distortion; andquantifying and correlating the second image with respect to the firstimage.
 5. The method of claim 4, wherein correcting the second imageincludes applying via a cloud based server system, linear transformationof position vectors on the second image.
 6. The method of claim 4,wherein linearizing the plurality of color regions includes applying viaa cloud based server system, linear transformation on at least theplurality of color regions of the second image.
 7. The method of claim1, wherein the object is a diagnostic assay.
 8. The method of claim 7,further comprising: analyzing the second image to collect quantifiabledata from the second image, the quantifiable data relating to a testassociated with the diagnostic assay.
 9. The method of claim 1, whereinthe fiducial marker is an adhesive label including the plurality ofcolor regions.
 10. The method of claim 1, wherein the unique identifieris at least one of a barcode, a hologram, and magnetic ink characters.11. The method of claim 1, wherein the first image is captured undercontrolled conditions.
 12. The method of claim 1, wherein spectralinformation includes at least one of intensity, wavelength, spectralvalue, Doppler shift, and Zeeman splitting.
 13. The method of claim 1,wherein resampling the second image includes at least one of colorcorrection, white balancing, gray balancing, gamma correction, andaffine image transformation.
 14. A method for collecting reliable datarelating to a test from a second image, the method comprising: disposinga unique identifier on a diagnostic assay; disposing a spectral fiducialon the diagnostic assay, the spectral fiducial including a plurality ofcolor regions; capturing a first image of the diagnostic assay with afirst camera; at a processor: receiving the second image of thediagnostic assay, the second image captured with a second camera;associating the second image with the first image based on the uniqueidentifier; comparing spectral information of at least a portion of thespectral fiducial in the second image with spectral information of atleast the portion of the spectral fiducial in the first image;resampling the second image to match at least the portion of thespectral fiducial in the second image to at least the portion of thespectral fiducial in the first image based on the comparison; andcorrelating spectral information of at least a portion of the resampledsecond image to corresponding information relating to the test tocollect reliable data, wherein the test is associated with thediagnostic assay.
 15. The method of claim 14, wherein the diagnosticassay is a lateral flow immunochromatographic assay.
 16. The method ofclaim 14, wherein the spectral fiducial is an adhesive label includingthe plurality of color regions.
 17. The method of claim 15, wherein thelateral flow immunochromatographic assay includes a control area and atest area.
 18. The method of claim 17, wherein the at least a portion ofthe resampled second image is the test area in the resampled secondimage.
 19. The method of claim 15, wherein the lateral flowimmunochromatographic assay is a medical diagnostic assay used for atleast one of home testing, point of care testing, and laboratorytesting.
 20. The method of claim 14, wherein spectral informationincludes at least one of intensity, wavelength, spectral value, Dopplershift, and Zeeman splitting.
 21. The method of claim 14, whereinresampling the second image includes at least one of color correction,white balancing, gray balancing, gamma correction, and affine imagetransformation.
 22. The method of claim 14, further comprising:correcting the second image for any physical distortion; and quantifyingand correlating the second image with respect to the first image. 23.The method of claim 22, wherein correcting the second image includesapplying via the processor, linear transformation of position vectors onthe second image.
 24. The method of claim 22, wherein linearizing theplurality of color regions includes applying via the processor, lineartransformation on at least the plurality of color regions of the secondimage.
 25. The method of claim 14, wherein correlating includes applyinga curve fitting formula on spectral information of the at least aportion of the resampled second image.
 26. The method of claim 14,wherein the unique identifier is at least one of a barcode, a hologram,and magnetic ink characters.
 27. The method of claim 14, wherein thefirst image is captured under controlled conditions.
 28. The method ofclaim 14, further comprising: storing the first image in a cloud basedserver database.
 29. The method of claim 14, wherein the reliable dataincludes specification, label, and quantity of an analyte.
 30. Themethod of claim 14, wherein the processor is a cloud based serverprocessor.